1988年澜沧—耿马地震的预报速报与对策

1988年11月6日,在云南省澜沧、耿马相继发生了7.6级、7.2级地震。这次地震前曾经作过准确的中长期预报。在1985年完成1986年出版的南北地震带未来十年地震危险性研究报告和预测图中,澜沧、耿马地区就是两个圈定的7级危险区中的一个。1986年该报告正式上报云南省人民政府。1987年起该区域为我省的地震危险性监视区。1988年除加强监视外,我们还加强了一系列大震对策措施的准备工作。1988年8月该区地震活动增强,我局派出的现场考察组向当地政府作了汇报,并指出该区仍存在着严重的地震危险性。与此同时,省局科技人员对该区作出了一系列不同程度的短期预报,并向当地打了招呼,当地政府采取了一定的措施。遗憾未能作出临震预报。大震发生后,我局昆明遥测台网仅7分钟定了位置和震级,并报告了国家地震局和省政府,及时为抗震救灾提供可靠的科学依据。震后2.5小时,现场监测队伍就出发了。在大震现场仅三天内就作出了地震趋势判断,也较好地作出了强余震的预报,有力地组织了地震考察及地震知识宣传。这一系列的大震对策工作极大地推动了现场的抗震救灾工作,减轻了震灾的继续发生及其带来的巨大的心理和社会影响。整个澜沧—耿马地震的预报、速报及大震对策工作在国内外都是一次较高水平的科学实践,得到了中央慰问团及     

基于概率积分法的潜山型地裂缝空间预测方法研究

苏南地区锡北镇地裂缝灾害活跃,且为典型的基岩潜山型抽水沉降地裂缝。以无锡锡北镇杨墅里地裂缝为研究对象（31.703174°~31.705488°N,120.452707°~120.453410°E）,基于非连续介质理论,运用概率积分方法建立基岩潜山条件下的抽水沉降裂缝空间预测数学模型。运用该理论模型计算得出基岩潜山上覆岩土体的差异性沉降规律、地表倾斜程度及地表曲率变化规律,并指出地表倾斜程度与地表曲率为地裂缝易发位置的重要评价指标。将地裂缝空间预测数学模型计算出的沉降规律、地裂缝发生位置等结果与物理模型试验结果进行对比研究,结果显示：1）当W"（x）=0时,地表倾斜函数取得极值,此时x=±0.8 m,表明距模型左边界2.6 m与4.2 m处地表倾斜程度最大,差异性沉降最为明显。2）当W"'（x）=0时,地表曲率函数取得极值,x=0,表明在距模型左边界3.4 m处地表曲率最大,为拉应力集中位置。3）模型试验地裂缝集中发生2.4~2.6 m、3.3~3.5 m和3.9~4.2 m这3处;基于概率积分方法的潜山型地裂缝空间预测结果与物理模型试验结果基本一致。预测模型能够合理解释物理模型试验中潜山山腰及潜山山顶三处地裂缝的发生位置,验证了其正确性,为地裂缝空间预测方法提供了新的研究思路。

     

一种利用测井资料预测岩石可钻性的计算机方法

介绍了一种利用测井资料快速求得岩石可钻性级值的计算机方法。该方法能够比较准确地预测岩石可钻性，快速做出连续的地层可钻性剖面图。室内微钻头可钻性试验和江汉油田现场应用验证表明：该系统具有比较高的可靠性，是可以投入油田现场应用的。     

滑坡定量预测的非线性理论方法

滑坡时空预测是当前滑坡研究中的难题，特别是滑坡时间预测工作，其进展缓慢。本文基于非线性科学理论，分析了滑坡活动的分形特征及时间分形预测方法，研究了滑坡空间预测的人工神经网络特征，系统介绍了滑坡时间预测的非线性动力学理论。在此基础上，讨论了滑坡定量预测的发展趋势。     

长波辐射(OLR)信息预测华北地震的方法研究

利用卫星对地遥测长波辐射(OLR)信息,分析了华北地块OLR信息场的空间分布及其变化特征,结合地震活动分布特点进行了短期预测中强地震判定指标的方法研究。结果表明该方法可对华北地区短期内有无可能发生M5以上的显著地震做出较为明确的诊断。经近一年的实际检验效果良好,对华北地区震情监测预报有实用价值。     

云南地区综合地震前兆信息量及其短期映震能力分析

选取了云南地区34个台站的55项映震能力较好的形变、地磁和地下流体前兆观测资料,计算并提取了这些前兆资料的单项及综合地震前兆信息量,并对其采取一致性的异常判定标准,统计异常对云南地区M≥6．0地震的映震情况及预测效能,进而确定了云南地区M≥6．0地震的短期综合预报指标。     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

基于格林函数理论的波场预测和鬼波压制方法

作为一种特殊的噪声,鬼波对一次波的波形及频带宽度产生极大的影响,鬼波压制是提高海上地震资料分辨率及保真度的重要因素.以格林公式为基础,详细论述了基于格林函数理论的鬼波压制方法,在不需要地下介质信息的条件下,进行地震数据驱动鬼波压制,并根据"Double Dirichlet"(双狄利克雷)边界条件,预测压力波场和垂直速度波场.建立了基于格林函数理论鬼波压制的处理流程,数值模拟和实际资料处理结果表明,基于格林函数理论鬼波压制方法在很好地去除鬼波的同时极大地拓宽了地震资料的频带,尤其提升了低频端能量,有利于后续资料的处理解释.     

黄渤海底层盐度预报方法的研究

根据二维流体动力学方程和深度平均盐度扩散方程在河口径流量以及蒸发和降水之差为已知情况下构成的闭合方程组,预报出深度平均盐度,然后利用底层盐度与深度平均盐度、水深和时间(月)之间的经验关系,给出底层盐度的二维预报。为了检验试报结果的可靠性,文中将黄渤海底层盐度的试报结果(1979年7月11日,时效为3d)与标准断面观测资料(7月4—14日观测,124.5°E以西,共104站)作一粗略比较。比较表明,试报结果与实测值的相关系数为0.96,均方误差为α=0.26,绝对误差小于0.2和0.3的站数分别占总站数的63.5％和77.0％,而总均绝差为0.19。由此可见,试报的效果是令人满意的。     

Instability model for recurring large and great earthquakes in southern California

The locked section of the San Andreas fault in southern California has experienced a number of large and great earthquakes in the past, and thus is expected to have more in the future. To estimate the location, time, and slip of the next few earthquakes, an earthquake instability model is formulated. The model is similar to one recently developed for moderate earthquakes on the San Andreas fault near Parkfield, California. In both models, unstable faulting (the earthquake analog) is caused by failure of all or part of a patch of brittle, strain-softening fault zone. In the present model the patch extends downward from the ground surface to about 12 km depth, and extends 500 km along strike from Parkfield to the Salton Sea. The variation of patch strength along strike is adjusted by trial until the computed sequence of instabilities matches the sequence of large and great earthquakes sincea.d. 1080 reported by Sieh and others. The last earthquake was theM=8.3 Ft. Tejon event in 1857. The resulting strength variation has five contiguous sections of alternately low and high strength. From north to south, the approximate locations of the sections are: (1) Parkfield to Bitterwater Valley, (2) Bitterwater Valley to Lake Hughes, (3) Lake Hughes to San Bernardino, (4) San Bernardino to Palm Springs, and (5) Palm Springs to the Salton Sea. Sections 1, 3, and 5 have strengths between 53 and 88 bars; sections 2 and 4 have strengths between 164 and 193 bars. Patch section ends and unstable rupture ends usually coincide, although one or more adjacent patch sections may fail unstably at once. The model predicts that the next sections of the fault to slip unstably will be 1, 3, and 5; the order and dates depend on the assumed length of an earthquake rupture in about 1700.